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12th International Conference on Recent Trends in Computing, ICRTC 2024

作者： Vijay, K. Sorna Shanthi, D. Jaeyalakshmi, M. Vignesh, P. Yuvaraja, M. Department of Artificial Intelligence and Machine Learning Rajalakshmi Engineering College Chennai India Department of Artificial Intelligence and Data Science Rajalakshmi Engineering College Chennai India Department of Computer Science and Engineering Rajalakshmi Engineering College Chennai India

ISBN: (纸本)9789819789450

The digital era has made seamless sharing and keeping of media such as images on cloud platforms an integral part of our lives. Still, there is a big issue about user privacy and data security in these repositories. We have thus created a very strong system that is able to do image encryption and decryption so as to protect the privacy of users while at the same time making their pictures accessible. Our system uses hybrid encryption where we try to prevent even cloud service providers from accessing the original files hence ensuring that only the owner who has been authenticated through password based authentication can decrypt or access them. In this way, it allows maintaining high privacy standards without sacrificing user convenience. It starts with preparing photos for encryption so that they are kept safe during transmission and storage. When needed by a user, images are decrypted by our system and become available only to authorized users. Consequently, users can comfortably leave their private photo albums on cloud storage systems knowing too well that their friends cannot access them thereby increasing trust in using cloud services for storing sensitive information. The aim of this work is to increase user privacy and data security in cloud repositories through employing this encryption technique. The users are guaranteed that their private details are not exposed to others enabling them to use cloud storage efficiently without interfering with their privacy at all. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Cloud platforms

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Integrating CNN and XGBoost with Synthetic Samples for Advanced Android Malware Detection 15

Integrating CNN and XGBoost with Synthetic Samples for Advan...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Gadilohar, Pragat Tomar, Deepak Singh Dehalwar, Vasudev Sharma, Yogesh Kumar Department of Centre for Artificial Intelligence Bhopal India Department of Computer Science & Engineering Bhopal India

ISBN: (纸本)9798350370249

The ubiquitous presence of Android smartphones exposes users to an ever-expanding arsenal of malware threats. Existing detection methods often struggle with false positives and limited adaptability to emerging threats. This paper presents a novel hybrid deep learning approach for Android malware detection, achieving a remarkable 98.66% accuracy while minimizing false positives. By combining Convolutional Neural Networks (CNNs) with XGBoost and leveraging Generative Adversarial Networks (GANs) for data augmentation, this method demonstrates scalability and robustness across varying dataset sizes. This approach significantly enhances user privacy, device security, and app integrity, contributing to the ongoing battle against evolving Android malware threats. Utilizing a balanced dataset of 3,000 benign and malicious APK files, our method employs feature importance techniques to ensure model interpretability and relevance. The proposed approach showcases consistent performance with 1000, 2000, and 3000 APK files, highlighting its efficacy in safeguarding Android users against an ever-expanding array of malware threats. © 2024 IEEE.

关键词： Android malware CNN Dalvik opcode F-classification GANs Permutation Importance XGBoost

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Automatic segmentation, classification, and prediction of pelvic bone tumors using deep learning techniques

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Journal of engineering and Applied Science 2024年第1期71卷 214页

作者： Shouman, Mona Rahouma, Kamel Hussein Hamed, Hesham F. A. Computer and System Engineering Department Faculty of Engineering Minia University Minia Egypt Faculty of Engineering Minia University Minia Egypt College of Artificial Intelligence Russian University Cairo Egypt

MRI and CT images are widely utilized for detecting tumors in internal organs. However, accurately distinguishing tumors using these images only, poses a challenge. This demands human expertise to effectively mark tumors in a given MRI or CT image, which is time-consuming. In this study, MONAI Label and MONAI Dynamic U-Net (DynU-Net) were used to segment tumors in any given 3D CT image of the pelvic bone, aiming to assist radiologists further. This method helps to run the model without needing a GPU which is better than traditional approaches. In addition, a convolutional neural network (CNN) was used to classify the tumors as benign or malignant and to predict three grades of tumors (low, medium, and high). The use of CNN in classification and prediction gives higher results than other studies. A dataset of 178 3D CT picture images was employed to feed the networks with the help of Adam optimizer and Categorical cross-entropy. We employ a set of 3D CT scans because of their greater contrast and spatial resolution which is better used for pelvic bone tumors. The Dice similarity coefficient (DSC) and Hausdorff distance (HD) have been used to perform calculations in segmentation. The results show a DSC of 0.7660280 and an HD of 16.75480. A range of performance metrics, such as sensitivity, specification, and F1-score for classification and prediction methods, are used to evaluate the accuracy of the proposed system. The system has accuracy (99.4%) for classification and (97.8%) for prediction. These findings indicate that MONAI Label is effective for automatic segmentation of tumors in a given CT scan;with high accuracy. Moreover, CNN is useful for classification and prediction systems with high accuracy. However, achieving better results is possible with an abundance of training samples. © The Author(s) 2024.

关键词： computerized tomography

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Occluded human pose estimation based on limb joint augmentation

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Neural Computing and Applications 2025年第3期37卷 1241-1253页

作者： Han, Gangtao Song, Chunxiao Wang, Song Wang, Hao Chen, Enqing Wang, Guanghui School of Electrical and Information Engineering Zhengzhou University Zhengzhou China School of Computer and Artificial Intelligence Zhengzhou University Zhengzhou China Department of Computer Science Toronto Metropolitan University Toronto Canada

Human pose estimation aims at locating the specific joints of humans from the images or videos. While existing deep learning-based methods have achieved high positioning accuracy, they often struggle with generalization in occlusion scenarios. In this paper, we propose an occluded human pose estimation framework based on limb joint augmentation to enhance the generalization ability of the pose estimation model on the occluded human bodies. Specifically, the occlusion blocks are at first employed to randomly cover the limb joints of the human bodies from the training images, imitating the scene where the objects or other people partially occlude the human body. Trained by the augmented samples, the pose estimation model is encouraged to accurately locate the occluded keypoints based on the visible ones. To further enhance the localization ability of the model, this paper constructs a dynamic structure loss function based on limb graphs to explore the distribution of occluded joints by evaluating the dependence between adjacent joints. Extensive experimental evaluations on two occluded datasets, OCHuman and CrowdPose, demonstrate significant performance improvements without additional computation cost during inference. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Joints (anatomy)

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Exploration of Diffusion-Based Test Case Generation of Autonomous Driving Systems

Exploration of Diffusion-Based Test Case Generation of Auton...

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2025 International Conference on Electronics, Information, and Communication, ICEIC 2025

作者： Lee, Joonwoo Uk-Jin Lee, Scott Hanyang University Major in Bio-Artificial Intelligence Department of Applied Artificial Intelligence Ansan Korea Republic of Hanyang University Department of Computer Science and Engineering Ansan Korea Republic of

ISBN: (纸本)9798331510756

Autonomous driving systems demand extensive testing to guarantee safety and reliability. However, real-world testing is often costly and limited in variety. This paper investigates the use of diffusion models to generate synthetic driving images as a means of augmenting test datasets for autonomous driving systems. Leveraging their capability to produce diverse and highly realistic images, diffusion models can simulate various driving conditions, including rare or challenging scenarios that are difficult to replicate. By incorporating these synthetic images into the testing pipeline, self-driving systems can be assessed across a wider range of conditions, enhancing their robustness and safety. © 2025 IEEE.

关键词： Autonomous Driving Diffusion Test Generation

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Predicting the impact of no. of authors on no. of citations of research publications based on neural networks

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Journal of Ambient intelligence and Humanized Computing 2023年第7期14卷 8499-8508页

作者： Talaat, Fatma M. Gamel, Samah A. Faculty of Artificial Intelligence Kafrelsheikh University Kafrelsheikh Egypt Department of Computer Engineering and Systems Faculty of Engineering Mansoura University Mansoura Egypt

Any work's citations are regarded as a key characteristic that leads to its appraisal and study. Citations are one of the most important indicators of a research publication's quality. Citations can have a favorable or bad impact on any piece of work or publication depending on a variety of circumstances, including author skill, publication venue, research topic, and so on. The goal of this study is to see how the number of co-authors affects the number of citations in research papers. There will be a correlation analysis between the number of co-authors and the number of citations for research articles, and we will observe how the number of co-authors affects the number of citations for publications. Citation data is gathered from databases such as DBLP, ACM, MAG (Microsoft Academic Graph), and others. There are 629,814 papers and 632,752 citations in the initial version. We use two methods to examine the impact of co-author count on the number of citations in a research paper: (i) Pearson’s correlation coefficient (PCC), and (ii) multiple regression (MR). To test the impact of co-author count on citation count of research publications, we calculate Pearson’s correlation coefficient (ra) between the two variables number of authors (NA) and citation count (CC). We also calculate Pearson’s correlation coefficient between the citation count (CC) and the most effective variables to compare between the impact of the number of authors and the impact of the other factors such as (i) rc between number of countries (NC) and citation count (CC). (ii) rv between venue category (VC) and citation count (CC). (iii) ry between Year_From (YF) and citation count (CC). Empirical evidence shows that co-authored publications achieve higher visibility and impact. To predict the number of citations from the previously mentioned factors (NA, NC, VC, and YF), we use multiple linear regression (MLR). The goal of multiple linear regression (MLR) is to model the linear relationship between t

关键词： Multiple linear regression

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Frozen or Fine-tuned? Analyzing Deep Learning Models and Training Strategies for Optimizing Big Five Personality Traits Prediction from Text 10

Frozen or Fine-tuned? Analyzing Deep Learning Models and Tra...

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10th International Conference on artificial intelligence and Robotics, QICAR 2024

作者： Soleimani, Masoud Kashani, Hamidreza Baradaran Department of Computer Engineering University of Isfahan Isfahan Iran Artificial Intelligence Department Faculty of Computer Engineering University of Isfahan Isfahan Iran

ISBN: (纸本)9798350348873

In today's digital age, the comprehension and prediction of human personality traits have assumed paramount significance. This study embarks on the task of forecasting the Big Five personality traits through textual data, harnessing the capabilities of advanced natural language processing models. The focal dataset is the ChaLearn First Impressions V2, a treasure trove of human-generated text coupled with Big Five personality trait labels. A diverse array of models undergo scrutiny, ranging from basic deep learning models like Deep Pyramid Convolutional Neural Network (DPCNN) and Hierarchical Attention Network (HAN) to cutting-edge transformer-based architectures such as BERT and FLAN-T5. These models undergo meticulous evaluation across various training scenarios, spanning scenarios where all layers are fine-tuned, only the embedding layer is freezed, and the complete layer freezing, with exclusive attention to Transformer models. Notably, models such as DPCNN and HAN emerge as stars, boasting remarkable accuracy attributable to their prowess in hierarchical feature extraction. Conversely, Transformer models like ELECTRA shine when layers remain frozen, showcasing their exceptional contextual comprehension. Furthermore, the study employs word clouds to visually encapsulate the essence of each Big Five personality trait, unraveling intricate relationships between specific words and these traits. The findings underscore the intricate interplay among model architecture, training methodologies, and layer freezing, offering valuable insights into strategies that yield optimal performance in predicting personality traits. In an age dominated by digital communication, this research contributes significantly to our understanding and prediction of human personalities. ©2024 IEEE.

关键词： Forecasting

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Machine learning based Parkinson’s disease detection and progression evaluation using gray and white matter segmentation from 3D MRI

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Journal of engineering and Applied Science 2024年第1期71卷 190页

作者： Islam, Nair Ul Khanam, Ruqaiya Department of Computer Science & amp Engineering Centre of Excellence for Artificial Intelligence in Medicine Imaging & ampForensics Sharda University Greater Noida India Department of Electrical Electronic & ampCommunication Engineering Centre of Excellence for Artificial Intelligence in Medicine Imaging & ampForensics Sharda University Greater Noida India

Parkinson’s disease (PD) is a movement-related neurological condition caused by the death of brain nerve cells that produce dopamine. T1 MR images were obtained from the Parkinson’s Progression Markers Initiative (PPMI) database. Data was collected at baseline, and at 48 months duration. SPM12 software was used to segment gray matter (GM) and white matter (WM) from the MR images. For the classification of PD, CNN and machine learning (ML) classifiers were used to train on the segmented GM and WM. The findings demonstrated that utilizing the segmented GM and WM obtained from MR images at 48 months had a better predictive ability than the data obtainment at the baseline. CNNs did not perform as well as the conventional ML algorithms, especially for the baseline data. This result is most likely due to the smaller dataset available for training the CNNs, as CNNs normally require more data for best performance. Overall, machine learning algorithms were able to distinguish between people with Parkinson’s disease and healthy controls by analyzing GM and WM in brain scans. The classifiers trained at different stages demonstrated varying degrees of accuracy, with the predictive accuracy for the 48-month dataset surpassing that of the baseline data by a significant margin. The accuracy rate for GM was 65.78% at baseline and improved significantly to 92.59% at 48 months. Similarly, the accuracy rate for WM was 60.52% at baseline and improved to 88.89% at 48 months. © The Author(s) 2024.

关键词： Magnetic resonance imaging

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Text-Image Feature Fine-Grained Learning for Joint Multimodal Aspect-Based Sentiment Analysis

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computers, Materials & Continua 2025年第1期82卷 279-305页

作者： Tianzhi Zhang Gang Zhou Shuang Zhang Shunhang Li Yepeng Sun Qiankun Pi Shuo Liu School of Data and Target Engineering Information Engineering UniversityZhengzhou450001China Information Engineering Department Liaoning Provincial College of CommunicationsShenyang110122China School of Computer and Artificial Intelligence Zhengzhou UniversityZhengzhou450000China

Joint Multimodal Aspect-based Sentiment Analysis(JMASA)is a significant task in the research of multimodal fine-grained sentiment analysis,which combines two subtasks:Multimodal Aspect Term Extraction(MATE)and Multimodal Aspect-oriented Sentiment Classification(MASC).Currently,most existing models for JMASA only perform text and image feature encoding from a basic level,but often neglect the in-depth analysis of unimodal intrinsic features,which may lead to the low accuracy of aspect term extraction and the poor ability of sentiment prediction due to the insufficient learning of intra-modal *** this problem,we propose a Text-Image Feature Fine-grained Learning(TIFFL)model for ***,we construct an enhanced adjacency matrix of word dependencies and adopt graph convolutional network to learn the syntactic structure features for text,which addresses the context interference problem of identifying different aspect ***,the adjective-noun pairs extracted from image are introduced to enable the semantic representation of visual features more intuitive,which addresses the ambiguous semantic extraction problem during image feature ***,the model performance of aspect term extraction and sentiment polarity prediction can be further optimized and *** on two Twitter benchmark datasets demonstrate that TIFFL achieves competitive results for JMASA,MATE and MASC,thus validating the effectiveness of our proposed methods.

关键词： Multimodal sentiment analysis aspect-based sentiment analysis feature fine-grained learning graph convolutional network adjective-noun pairs

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DGeC: Dynamically and Globally Enhanced Convolution

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on artificial intelligence 2025年第4期6卷 921-933页

作者： Zhang, Zihang Liu, Yuling Zhou, Zhili Yang, Gaobo Liao, Xin Wu, Q. M. Jonathan Hunan University College of Computer Science and Electronic Engineering Changsha410082 China Guangzhou University Institute of Artificial Intelligence and Blockchain Guangzhou510006 China University of Windsor Department of Electrical and Computer Engineering WindsorON250101 Canada

We explore the reasons for the poorer feature extraction ability of vanilla convolution and discover that there mainly exist three key factors that restrict its representation capability, i.e., regular sampling, static aggregation, and limited receptive field. With the cost of extra parameters and computations, existing approaches merely alleviate part of the limitations. It drives us to seek a more lightweight operator to further improve the extracted image features. Through a closer examination of the convolution process, we discover that it is composed of two distinct interactions: spatial-wise interaction and channel-wise interaction. Based on this discovery, we decouple the convolutional blocks into these two interactions which not only reduces the parameters and computations but also enables a richer ensemble of interactions. Then, we propose the dynamically and globally enhanced convolution (DGeC), which includes several components as follows: a dynamic area perceptor block (DAP) that dynamically samples spatial cues, an adaptive global context block (AGC) that introduces the location-aware global image information, and a channel attention perceptor block (CAP) that merges different channel-wise features. The experiments on ImageNet for image classification and on COCO-2017 for object detection validate the effectiveness of DGeC. As a result, our proposed method consistently improves the performance with fewer parameters and computations. In particular, DGeC achieves a 3.1% improvement in top-1 accuracy on ImageNet dataset compared to ResNet50. Moreover, with Faster RCNN and RetinaNet, our DGeC-ResNet50 also consistently outperforms ResNet and ResNeXt. © 2024 IEEE.

关键词： Convolutional neural networks

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